Codebase Indexing Beats Memory Banks Because Summaries Drift

Memory banks drift. Code doesn't.

That's the difference between an AI that knows what your codebase was and one that knows what it is.

The mismatch problem

You're debugging a failing integration test. The AI confidently references a helper function that doesn't exist anymore. You renamed it three weeks ago during a refactor. The memory bank still has the old name cached in a summary.

The model isn't hallucinating. It's following outdated guidance. And there's no mechanism to detect the mismatch.

This is the memory bank failure mode: summaries capture a snapshot, but codebases evolve. Every rename, every deleted file, every refactored module creates drift between what the memory bank "knows" and what actually exists in your project.

"Codebase indexing provides an understanding of what your codebase currently is, whereas a memory bank does not. It provides an understanding of what a bunch of summaries have indicated your codebase is."

Hannes Rudolph, Office Hours S01E08

The sync gap

Memory banks require manual updates. You tell the model to update a specific section, and sometimes it appends instead of replacing. You end up with contradictory information in the same memory file: the old architecture and the new one, sitting side by side.

"With memory bank you have to do like I've done this: you should update this memory bank, and sometimes it fails because I've tried using memory bank in the past and then like I told it please update this specific part and instead of updating it just appends the thing that I want to replace."

Ellie, Office Hours S01E08

Codebase indexing solves this by syncing with your project automatically. Add a file, the index updates. Rename a function, the index reflects it. The queries return results from the current state, not cached summaries that may or may not match reality.

The detection problem

Here's the part that compounds the drift: if something in the memory bank is inaccurate, the AI has no way to detect that.

"If there is something inaccurate in the codebase, there's no way for Roo to detect that."

Hannes Rudolph, Office Hours S01E08

The model treats the memory bank as ground truth. If the summary says "auth is handled in /lib/auth.js" but you moved it to /services/authentication/ last sprint, the model will confidently reference the wrong location. It won't check. It can't check. The summary is what it knows.

With indexing, the query hits the actual file structure. If the file doesn't exist, the search returns nothing. If it was renamed, the new name appears. The index is a lens on reality, not a cache of what someone once wrote down.

The tradeoff

Indexing isn't free. It requires compute to build and update the index. For large repositories, initial indexing takes time. And the index reflects file structure and content, not high-level architectural decisions.

Memory banks can capture things indexing can't: design rationale, context about why a decision was made, team conventions that don't live in code comments. For that kind of knowledge, you still need some form of explicit documentation.

The question is what you're trying to solve. If you need the AI to understand what code exists right now, indexing wins. If you need it to understand why the code was written that way, you need documentation alongside.

How Roo Code closes the loop with live context

Roo Code uses codebase indexing to maintain accurate context as your project evolves. Rather than relying on static summaries that drift, the index reflects your actual file structure, function names, and code organization in real time.

This matters because Roo Code closes the loop: it proposes diffs, runs commands and tests, and iterates based on results. That loop depends on accurate context. When the AI suggests changes to a file that was renamed or references a function that no longer exists, the loop breaks. You spend time correcting the AI instead of reviewing its work.

Codebase indexing eliminates the stale context failure mode that breaks agentic workflows. With BYOK (bring your own key), you control the compute cost of indexing while getting context that matches your current codebase state.

Memory banks vs. codebase indexing

Why this matters for your team

For a team shipping daily across a codebase that's actively evolving, the drift problem compounds. Each stale summary creates a small failure mode. A developer asks about the auth flow, gets outdated guidance, spends twenty minutes figuring out why the suggested approach doesn't work.

Multiply that by the number of developers, the number of queries per day, and the velocity of your codebase changes. The cost of stale context adds up.

Codebase indexing doesn't eliminate the need for documentation. But it eliminates the failure mode where your AI is confidently wrong about what files exist and where they are.

If your AI is confident about code that doesn't exist anymore, check whether it's running on memory banks or live indexing. The difference shows up the moment your codebase changes.